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Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm

ABSTRACT: Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm

PROVIDER: PRJNA414289 | ENA |

REPOSITORIES: ENA

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Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm [RNA-Seq]

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Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm [RNA-Seq]

Project description:In this study, we compare the performance of RNA-seq (Illumina HiSeq) and junction arrays (Affymetrix Human Transcriptome array) for the analysis of splicing events. Three different cell lines were treated with CX-4945, a drug that severely affects splicing. To make a fair comparison, we developed EventPointer, an algorithm that detects and labels alternative splicing events in both junction arrays and RNA-seq. Common results and discrepancies between both technologies were validated or resolved by more than 200 PCRs.

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Development of a novel splice array platform and its application in the identification of alternative splice variants

Project description:Changes in alternative splicing are associated with several pathological conditions, including cancer. Microarrays strategies, which allow for the characterization of thousands of alternative splice forms in a single test, can be applied to identify differential alternative splicing events. In this study, a novel splice array platform was developed, including the design of a high-density oligonucleotide array, a labeling procedure, and an algorithm to identify splice events. The array consists of exon probes and thermodynamically balanced junction probes. Suboptimal probes are tagged and considered in the final analysis. An unbiased labeling protocol was developed using random primers. The algorithm used to distinguish changes in expression from changes in splicing was calibrated using internal non-spliced control sequences. The performance of this splice array was first validated with artificial constructs for CDC6, VEGF, and PCBP4 isoforms. The platform was then applied to the analysis of differential splice forms for 8000 genes in lung cancer samples compared to matched normal lung tissue. The expression of lung cancer-associated splice isoforms was validated by RT-PCR. Overexpression of splice isoforms was identified for genes encoding CEACAM1, FHL-1, MLPH, and SUSD2. None of these splicing isoforms had been previously associated with lung cancer. In conclusion, this highly accurate methodology enables the detection of alternative splicing events in complex biological samples, providing a powerful tool to identify novel diagnostic and prognostic biomarkers for cancer and other pathologies. 20 normal/tumor paired specimens. Tumor samples are from non-small cell lung cancer (NSCLC) whereas normals are from adjacent normal lung tissue.

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Cross-platform study

Project description:Gene expression microarrays have made a profound impact in biomedical research. The diversity of platforms and analytical methods has made comparison of data from multiple platforms very challenging. In this study, we describe a framework for comparisons across platforms and laboratories. We have attempted to include nearly all the available commercial and in house platforms. Using probe sequences matched at the exon level improved consistency of measurements across the different microarray platforms compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values as confirmed by QRT-PCR. Concordance of measurements was higher between laboratories on the same platform than across platforms. We demonstrate that, after stringent pre-processing, commercial arrays were more consistent than in-house arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms. This SuperSeries is composed of the SubSeries listed below.

2006-06-01 | GSE4854 | GEO

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Project description:Gene expression microarrays have made a profound impact in biomedical research. The diversity of platforms and analytical methods has made comparison of data from multiple platforms very challenging. In this study, we describe a framework for comparisons across platforms and laboratories. We have attempted to include nearly all the available commercial and “in-house” platforms. Using probe sequences matched at the exon level improved consistency of measurements across the different microarray platforms compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values as confirmed by QRT-PCR. Concordance of measurements was higher between laboratories on the same platform than across platforms. We demonstrate that, after stringent pre-processing, commercial arrays were more consistent than “in-house” arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms. Keywords: cross platform microarrays

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